Watercraft autopilot systems (e.g., controllers) typically rely on various primary components: a position sensor, a navigation aid device (NavAid), an autopilot device, and/or a sonar device (e.g., all of which may be provided as separate devices in communication and/or integrated with each other). The position sensor provides latitude and longitude with limited accuracy and bandwidth (e.g., through global positioning system (GPS) signals conventionally having approximately three meter accuracy and providing updates approximately once per second). The navigation aid device permits a user to define a desired reference path such as a track (e.g., a path between waypoints) and/or a contour (e.g., a sea bed depth along which to follow, possibly between waypoints). The sonar device permits a user to actively probe the sea bed rather than rely on chart data, which may not exist for the present geographical area. Conventional autopilot devices operate to minimize the deviation from the desired reference path.
Conventional reference path tracking/following algorithms that adjust heading simply as a function of cross track error (e.g., Heading=K∫CrossTrackErrordt; “when off track/contour to the right, turn to the left”) are inherently unstable because of a double integration occurring in the feedback loop. In this regard, a first integration occurs in the controller and a second integration occurs naturally (e.g., because cross track error builds/falls for a constant off-track angle). Each integration causes 90° of phase lag, so two integrations cause 180° of phase lag. As a result, the autopilot control signal is completely out of phase with the error and the phase margin is zero.
The inherently poor phase margin makes such conventional systems particularly susceptible to error due to noise and/or delay in the cross track signals. Yet, GPS data typically comes with high noise levels (e.g., 3 meter accuracy or worse), and the cross track error (XTE) signal can be delayed by 5 seconds or more when taking into account GPS receiver filtering, NavAid response times, and network delays. Furthermore, some watercraft do not respond immediately to the application of rudder or steering, and every conveyance has a different delay and a different rate of turn per unit of rudder applied, so the overall loop delays are variable and can be substantially worse than 5 seconds, making track keeping robustness and stability extremely challenging. Thus, conventional track keeping techniques are generally sluggish, mildly oscillatory, and unpredictable, particularly when large turns are required.
Directional control systems and methods are used to provide automated and/or supplemented control for planes, watercraft, and, more recently, automobiles. A significant drawback to conventional directional control systems is that they typically need to be designed and/or configured for a particular vehicle, and once configured, cannot easily be used to provide directional control for a different vehicle. Thus, manufacturing directional control systems and methods for a number of different vehicles, even if they are of the same type, such as different makes of ships, can be expensive due to extensive testing and adjustment procedures performed for each individual vehicle.
Adaptive control techniques have been developed to address manually performing the adjustment and testing procedures, but conventional adaptive techniques typically take too long to train to a particular vehicle dynamic under normal operating conditions. Furthermore, conventional adaptive techniques typically train to a very limited set of vehicle states and or dynamics, and directional controllers based on these techniques are known to drastically lose their accuracy and/or stability as conditions vary even subtly outside previous training conditions. Thus, there is a need for improved directional control methodologies.